The present invention relates to a method of extracting a signal. Such a method may be used to extract one or more desired signals from one or more contaminated signals received via respective communications channels. Signals may, for example, be contaminated with noise, with delayed versions of themselves in the case of multi-path propagation, with other signals which may or may not also be desired signals, or with combinations of these.
The communication path or paths may take any form, such as via cables, electromagnetic propagation and acoustic propagation. Also, the desired signals may in principle be of any form. One particular application of this method is to a system in which it is desired to extract a sound signal such as speech from contaminating signals such as noise or other sound signals, which are propagated acoustically.
WO 99/66638 discloses a signal separation technique based on state space modelling. A state space model is assumed for the signal mixing process and a further state space model is designed for an unmixing system. There is a suggestion that the communication environment for the signals may be time-varying, but this is not modelled in the disclosed technique.
U.S. Pat. No. 5,870,001 discloses a calibration technique for use in cellular radio systems. This technique is a conventional example of the use of Kalman filters.
U.S. Pat. No. 5,845,208 discloses a technique for estimating received power in a cellular radio system. This technique makes use of state space auto regressive models in which the parameters are fixed and estimated or “known” beforehand.
U.S. Pat. No. 5,581,580 discloses a model based channel estimation algorithm for fading channels in a Rayleigh fading environment. The estimator uses an auto regressive model for time-varying communication channel coefficients.
Kotecha and Djuric, “Sequential Monte Carlo sampling detector for Rayleigh fast-fading channel”, Proc. 2000 IEEE Int. Conf. Acoustics Speech and Signal Processing, Vol. 1, pages 61–64 discloses a technique for processing digital or discrete level signals. This technique makes use of modelling the system as a dynamic state space model in which channel estimation and detection of transmitted data are based on a Monte Carlo sampling filter. Channel fading coefficients and transmitted variables are treated as hidden variables and the channel coefficients are modelled as an autoregressive process. Particles of the hidden variables are sequentially generated from an importance sampling density based on past observations. These are propagated and weighted according to the required conditional posterior distribution. The particles and their weights provide an estimate of the hidden variables. This technique is limited to modelling of sources with fixed parameters.
Chin-Wei Lin and Bor-Sen Chen, “State Space Model and Noise Filtering Design in Transmultiplexer Systems”, Signal Processing, Vol. 43, No. 1, 1995, pages 65–78 disclose another state space modelling technique applied to communication systems. In a “transmultiplexer” scheme, desired signals are modelled as non-time-varying autoregressive processes with known and fixed parameters.